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A Study on Friction Coefficient Prediction of Hydraulic Driving Members by Neural Network  

김동호 (문경대학 자동차기계계열)
Publication Information
Transactions of the Korean Society of Machine Tool Engineers / v.12, no.5, 2003 , pp. 53-58 More about this Journal
Abstract
Wear debris can be collected from the lubricants of operating machinery and its morphology is directly related to the fiction condition of the interacting materials from which the wear particles originated in lubricated machinery. But in order to predict and estimate working conditions, it is need to analyze the shape characteristics of wear debris and to identify. Therefore, if the shape characteristics of wear debris is identified by computer image analysis and the neural network, The four parameter (50% volumetric diameter, aspect, roundness and reflectivity) of wear debris are used as inputs to the network and learned the friction. It is shown that identification results depend on the ranges of these shape parameters learned. The three kinds of the wear debris had a different pattern characteristic and recognized the friction condition and materials very well by neural network. We resented how the neural network recognize wear debris on driving condition.
Keywords
Wear Debris; Computer Image Analysis; Neural Network; 50% Volume Diameter; Moving Condition; Shape parameter; Friction Coefficient;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 /
[ Kim,D.S. ] / Neural Networks Thoeory an Application
2 Computer Image Analysis for Identification of Wear Particles /
[ Thomas,H.;Davies,A.D.;Luxmoore,A.R. ] / J. of Wear   DOI   ScienceOn
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[ Lee,S.S. ] / Trans. of KSMTE   과학기술학회마을
4 EHL and the Use of Image Analysis /
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5 Decision for Moving Condition of the Machine Driving System by Artificial Neural Network /
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[ Uedelhoven,W.;Franzl,M.;Guttenberger,J. ] / J. of Wear   DOI   ScienceOn
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[ Sefert,W.W.;Westcott,V.C. ] / J. of Wear   DOI   ScienceOn